This invention relates generally to data detectors and, more particularly, to automatic threshold level adjustment in data detectors.
Data detectors, such as threshold detectors (also known as slicers or transition detectors), are used in a wide variety of digital applications. These applications include data storage, retrieval, and transmission systems. Some of these systems use optical discs in lieu of traditional magnetic storage. Optical discs have many advantages over their magnetic counterparts, including easier portability and the ability to deliver multimedia content, games, and other data at a low cost.
Data is stored on an optical disc in the form of microscopic pits (or marks) and lands (or spaces), which separate neighboring pits. As the optical disc spins, the pits and lands pass over an optical laser beam. The pits and lands of the disc reflect the laser beam at varying intensities. The reflected beam is then detected by an optical pick-up unit and converted to a stream of binary data. Whenever the pick-up laser passes over a pit, a binary “0” is read. Whenever the pick-up laser passes over a land, a binary “1”, is read. The resulting system of encoded channel data is then converted to user data by a series of decoding steps.
Optical data systems, like other data storage and retrieval systems, are not immune to noise and channel distortion. As recording densities continue to increase relative to the wavelength of the light and other factors, the spectrum of the readback signal becomes band-limited relative to the channel data rate. As a result, the pick-up signal suffers from resolution loss. Other potential problems with optical channels are DC-offsets and signal asymmetry.
Signal asymmetry in optical channels results, in part, from irregular or incorrect pit or land sizes. For example, the lengths of the pits of an optical disc might be longer than their nominal length. This results in lands being correspondingly shorter than their nominal length. If not equalized or otherwise handled appropriately, signal asymmetry can result in increased detector decision errors, causing high data transfer error rates.
Because, in part, of the above channel distortions, traditional data slicers and threshold detectors use less than optimal threshold values. The threshold value in a data slicer or threshold detector determines when the optical pick-up signal is considered high or low. If the signal is below the detector's threshold, the signal may be estimated as a low signal. Similarly, if the optical pick-up signal is above or equal to the detector's threshold, this signal may be estimated as a high signal. Channel asymmetry can shift the optimal threshold value of the data detector.
A variety of techniques are used to find the optimal threshold of data slicers or threshold detectors. One technique is to adjust the detector's threshold based on the average of the positive and negative peaks of the data pick-up signal. Although this technique is simple to implement, it is not very effective for signal asymmetry since the peaks of the optical pick-up signal are typically unaffected by signal asymmetry.
Another technique used to find a data detector's optimal threshold is to incorporate non-linear equalization into the detector to help reduce channel distortion and noise. For example, decision feedback equalizers may be used to compute an error signal based on the detector decisions convolved with some model of the data channel. The detector's input is then adjusted based on the average of this error signal. Although this approach provides increased resolution, equalization techniques alone are still suboptimal because equalization does not reduce decision errors near transitions.
A final technique for adjusting the threshold value of a threshold detector involves sampling the optical pick-up signal during transitions. Using this technique, if the expected optical pick-up signal at a transition is zero, then the mean of the samples at the transition can be used as the detector's threshold value. Alternatively, a loop filter can be used to drive the average value of the pick-up signal transitions to zero. This technique works well for signal asymmetry because the mean of the transition samples is the most accurate and reliable threshold value.
Using a loop filter to drive the average value of the pick-up signal transitions to zero presents a problem. If the pick-up signal contains a large DC-offset, the loop filter may drive the average value of the pick-up signal transitions to one or more incorrect points. These incorrect transition points are called false locking points. If the detector locks to a one of these false locking points, the threshold detector will make many data decision errors, resulting in a high data transfer error rate.
Accordingly, it is desirable to provide a slicer bias loop that monitors characteristics of a detected signal in addition to the value at signal transitions in order to eliminate or reduce false locking points. The bias loop may be integrated with any data slicer or threshold detector to reduce decision errors and improve the detector's performance.